A semi-analytical method for the springback prediction of thick-walled 3D tubes

Zhang, Z.K.; Wu, Jianjun; Guo, Ruichao; Wang, M.Z.; Li, F.F.; Guo, Shaochang; Wang, Yiwen; Liu, W.P.

doi:10.1016/j.matdes.2016.03.026

Cited by 41 publications

(20 citation statements)

References 27 publications

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“…An effect of computational parameters on springback prediction by FE-based numerical simulation has been studied by Trzepiecinski and Lemu [ 7 ]. Theoretical [ 15 ], analytical [ 8 , 14 ], semi-analytical [ 16 ] and methods of artificial intelligence are also widely used to predict springback behaviour. Most analytical studies have focused on air bending while limited work has been done on V-die bending due to the inherent difficulties arising from the coining action associated with this problem [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Improving Prediction of Springback in Sheet Metal Forming Using Multilayer Perceptron-Based Genetic Algorithm

Trzepieciński

Lemu

2020

Materials

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This paper presents the results of predictions of springback of cold-rolled anisotropic steel sheets using an approach based on a multilayer perceptron-based artificial neural network (ANN) coupled with a genetic algorithm (GA). A GA was used to optimise the number of input parameters of the multilayer perceptron that was trained using different algorithms. In the investigations, the mechanical parameters of sheet material determined in uniaxial tensile tests were used as input parameters to train the ANN. The springback coefficient, determined experimentally in the V-die air bending test, was used as an output variable. It was found that specimens cut along the rolling direction exhibit higher values of springback coefficient than specimens cut transverse to the rolling direction. An increase in the bending angle leads to an increase in the springback coefficient. A GA-based analysis has shown that Young’s modulus and ultimate tensile stress are variables having no significant effect on the coefficient of springback. Multilayer perceptrons trained by back propagation, conjugate gradients and Lavenberg–Marquardt algorithms definitely favour punch bend depth under load as the most important variables affecting the springback coefficient.

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Section: Introductionmentioning

confidence: 99%

Improving Prediction of Springback in Sheet Metal Forming Using Multilayer Perceptron-Based Genetic Algorithm

Trzepieciński

Lemu

2020

Materials

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“…4. Compared with the arc-based method proposed by Zhang et al, 23 the helix-based method is preferable. First, the theoretical model considers the material hardening and thus can apply to most materials.…”

Section: Discussionmentioning

confidence: 99%

“…The same experiments as those conducted by Zhang et al 23 are used to verify the helix-based method (shown in Figure 11). According to the data of uniaxial tensile test, the power-hardening model is adopted and then the stress and strain relationship can be written as equation (50).…”

Section: Methodsmentioning

confidence: 99%

“…The theoretical model proposed achieves a better understanding of 3D tube forming and the discrete method makes it possible to predict the springback of 3D tubes. However, Zhang et al 23 have pointed out that some improvements need to be made. First, the elastic-perfectly plastic material model causes a great limitation of the application of this method since material hardening usually influences the accuracy of the springback predictions, especially for the materials with larger hardening index like stainless steel.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, the authors of this work presented a semi-analytical method (named arc-based method in this article) for springback predictions of thick-walled 3D tubes. 23 In the calculations of the bending and twisting moments, materials were assumed to be elastic-perfectly plastic. Deformation parameters (bending radii and twisting angles) of the tubes were obtained through a discrete method, by which the tube axis was divided into a large number of small elements and each element was approximated by an arc.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Springback predictions for three-dimensional thick-walled tubes based on the cylindrical helix splines

et al. 2017

The Journal of Strain Analysis for Engineering Design

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This article analyzed the forming process of thick-walled three-dimensional tubes under combined bending and twisting loads and performed the springback predictions based on the cylindrical helix splines. The equations to calculate the two kinds of moments consider the hardening effect, and they can be well applied to most materials. The cylindrical helix spline is adopted creatively to approximate the objective tube axis, and the curvature and the torsion of each cylindrical helix segment can represent the bending angle and the twisting angle per unit length of the corresponding tube element, respectively. All the cylindrical helix segments after springback are obtained based on the calculated moments and the final shape of the tube axis is achieved by connecting all of them through an appropriate splicing technology. Then, the whole deviations of the calculated shapes can be obtained. The method proposed is applied on a typical threedimensional tube forming and it has got good effect.

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Investigation to the torsion generation of spatial tubes in bending-twisting process

Zhang

Liang

et al. 2020

Int J Adv Manuf Technol

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A semi-analytical method for the springback prediction of thick-walled 3D tubes

Cited by 41 publications

References 27 publications

Improving Prediction of Springback in Sheet Metal Forming Using Multilayer Perceptron-Based Genetic Algorithm

Improving Prediction of Springback in Sheet Metal Forming Using Multilayer Perceptron-Based Genetic Algorithm

Springback predictions for three-dimensional thick-walled tubes based on the cylindrical helix splines

Investigation to the torsion generation of spatial tubes in bending-twisting process

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